Volume Profile Visible Range
An indicator to visualise price entries
#@title Imports
import pandas as pd
import numpy as np
from scipy import stats, signal
import plotly.express as px
import plotly.graph_objects as go
import requests
import datetime
import numpy as np
import matplotlib.ticker as ticker
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 300
import seaborn as sns
import random
sns.set()
# !pip install mplfinance
# More Plotting libaries
# from mplfinance.original_flavor import candlestick_ohlc
from matplotlib.pylab import date2num
from dateutil.relativedelta import relativedelta
# !pip uninstall yfinance
# !pip uninstall pandas-datareader
# !pip install yfinance --upgrade --no-cache-dir
# !pip install pandas-datareader
import yfinance as yf
#@title plot
def plot(tick,p=182,kk=False):
"""
Give a ticker and period in days, the VPVR is plotted and resistant lines are shown
"""
### Data functions
if (kk==False):
ticker = yf.Ticker(tick)
df = ticker.history(period=f'{p}d',interval='1d')
cp = df.Close[len(df)-1]
elif (kk==True):
link = f'https://openapi-v2.kucoin.com/api/v1/market/candles?type=1day&symbol={tick}'
r = requests.get(link)
df = pd.DataFrame.from_records(r.json()['data'])
df.columns = ['Time', 'Open', 'Close', 'High', 'Low', 'Volume', 'Turnover']
df['Time'] = pd.to_datetime(df['Time'], unit='s', errors='coerce')
df = df.set_index('Time')
df = df.head(p)
for col in df.columns:
df[col] = pd.to_numeric(df[col])
cp = df.Close[0]
### Density functions
kde_factor = 0.1
num_samples = 500
kde = stats.gaussian_kde(df['Close'],weights=df['Volume'],bw_method=kde_factor)
xr = np.linspace(df['Close'].min(),df['Close'].max(),num_samples)
kdy = kde(xr)
ticks_per_sample = (xr.max() - xr.min()) / num_samples
peaks,peak_props = signal.find_peaks(kdy)
pkx1 = xr[peaks]
pky1 = kdy[peaks]
min_prom = 0
peaks,peak_props = signal.find_peaks(kdy, prominence=min_prom)
pkx = xr[peaks]
pky = kdy[peaks]
left_base = peak_props['left_bases']
right_base = peak_props['right_bases']
line_x = pkx
line_y0 = pky
line_y1 = pky - peak_props['prominences']
### Plotting functions
sns.set_style("whitegrid")
plt.style.use('seaborn-dark-palette')
f, (ax1, ax2) = plt.subplots(1, 2,figsize=(20,5),gridspec_kw={'width_ratios': [1, 2.5]}, dpi= 300,
facecolor='w', edgecolor='k', sharey=True)
ax1.scatter(pky1,pkx1,marker='>',color='#06d6a0', zorder=10)
ax1.plot(kdy,xr,color='#d62828')
ax1.hist(df['Close'],weights=df['Volume'], bins=50, density=True,
orientation='horizontal', alpha = 0.1)
# print('='*50)
# print(f'{tick}')
proms = []
for x, y0, y1 in zip(pkx, pky, line_y1):
ax1.hlines(x,y0,y1, color='#06d6a0', alpha=1)
proms.append([x,y0-y1])
# print(f'This prominance level at {round(x,2)}:{round(prom,2)}')
# print('='*50)
#determine the strongest support
proms = pd.DataFrame.from_records(proms)
print()
proms.columns = ['p','pp']
sp = proms[proms['pp'] == proms['pp'].max()]['p'].reset_index()
print(tick,' | % distance from strongest support: ', round(100*((sp['p'][0]-cp)/cp),2))
# make the lines for the plot
nn = pky - line_y1
normalized_y1 = nn / np.sqrt(np.sum(nn**2))
for i, txt in enumerate(pkx1):
ax2.annotate(round(txt,2), (df.index[0], pkx1[i]))
for x,a in zip(pkx,normalized_y1) :
ax2.plot(df.index, [x]*len(df.index), color='#06d6a0', alpha= a) #change this normalised if you want gradients
ax2.plot(df.index, df['Close'], alpha = 1)
ax1.set_title(f'{tick} Volume Profile Visible Range (p={p}d)')
ax2.set_title(f'{tick} Price')
ax2.set_xlabel('Date')
ax1.set_xlabel('Volume profile (partial sums)')
ax1.set_ylabel('Price $')
ticks = ['BTC-USD','ETH-USD','ADA-USD','SOL1-USD','MATIC-USD', 'LINK-USD']
for tick in ticks:
plot(tick, p=365)
ticks = ['NDQ.AX','ASIA.AX','CLNE.AX','IOZ.AX','EMN.AX']
for tick in ticks:
plot(tick, p=365)
KT = ['DAG-USDT','LTX-USDT', 'COTI-USDT', 'UTK-BTC', 'OCEAN-BTC', 'ORN-USDT',
'ALEPH-USDT', 'CHR-USDT', 'NOIA-USDT','ALBT-USDT','UOS-USDT',
'CGG-USDT', 'AVA-USDT', 'SOUL-USDT', 'GOVI-USDT']
#hide_input
for tick in KT:
plot(tick, p=140, kk=True)
